Topics offered in WSDL 25
(Emphasis on hands-on & real-world applications)

• Basics of Python and libraries of importance.

• Basics of Deep Learning Library: PyTorch.

• Rudiments of Probability Theory for Machine Learning.

• Essentials of Matrix Calculus and Linear Algebra for Machine Learning.

• Bird’s Eye View of Machine Learning.

• Primer on Text, Video, and Image Data processing.

• Gradient-based Optimization techniques.

• Rudiments of Artificial Neural Networks and Backpropagation of Error.

• Tree-based Classifiers and ensemble techniques.

• Steps towards Deep Learning: Activation Functions, Normalization techniques, Regularization methods, and loss functions.

• Convolutional Neural Networks.

• Architectures of Deep Neural Network Models.

• Generative Deep Neural Network Models: GANs and VAEs

• Stable Diffusion and Score-based Generative Modeling

• Recurrent Neural Networks and Backpropagation through Time.

• Attention mechanism and Transformers: Where Text meets Vision.

• Large Language Models, pre-training, task adaptation, and fine-tuning. Multi-modal modeling.

• Introduction to Graph Neural Networks.

• Explainable and Trustworthy Artificial Intelligence 

• Contrastive Learning.

• Emerging Learning strategies: Semi-supervised, few-shot, zero-shot, etc.

• Adversarial Attacks, Defence, and Robust Deep Neural Network models.

• Theory of Deep Learning: Special Focus on GenAI.

• Deep Reinforcement Learning.

• Topological Data Analysis.

• Long-tailed Classification Problem. 

• Physics-informed Neural Networks.

• Neural Algorithmic Reasoning. 

• Introduction to Speech and Signal Processing. 

• Real-world Applications: From Problem to a Solution

• Image segmentation and Medical Data Analysis.

• Natural Language Understanding: Text Classification, question answering, summarization, etc.

• Working with LLMs: Tips and Tricks, quantization, approximation, RLHF etc.

• Prompt engineering.

• Business analytics and time-series forecasting.

• Graph data analysis.

• Sports Analytics

• Class Imbalanced Learning

• Speech and audio understanding